package ds_industry_2025.industry.gy_09.T3

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

/*
      2、编写scala代码，使用Spark根据dwd层的fact_produce_record表，基于全量历史增加设备生产一个产品的平均耗时字段
      （produce_per_avgtime），produce_code_end_time值为1900-01-01 00:00:00的数据为脏数据，需要剔除，并
      以produce_record_id和ProduceMachineID为联合主键进行去重（注：fact_produce_record表中，一条数据代表加工一个产品，
      produce_code_start_time字段为开始加工时间，produce_code_end_time字段为完成加工时间），将得到的数据提取下表所需
      字段然后写入dws层的表machine_produce_per_avgtime中，然后使用hive cli根据设备id降序排序查询前3条数据，将SQL语句复
      制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将执行结果截图粘贴至客户端桌面【Release\任务B提交
      结果.docx】中对应的任务序号下；
 */
object t5 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("t1")
      .config("hive.exec.dynamic.partition.mode","nonstrict")
      .config("spark.serializer","org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions","org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .config("spark.sql.legacy.parquet.datetimeRebaseModeInRead","LEGACY")
      .enableHiveSupport()
      .getOrCreate()

    val hdfs="hdfs://192.168.40.110:9000/user/hive/warehouse/hudi_gy_dwd.db/fact_produce_record"

    spark.read.format("hudi").load(hdfs)
      .where(col("producecodeendtime") !== "1900-01-01 00:00:00")
      .dropDuplicates(Seq("producerecordid","produceMachineID"))
      .createOrReplaceTempView("data")

    val result = spark.sql(
      """
        |select distinct
        |produce_record_id,
        |produce_machine_id,
        |produce_time,
        |avg(produce_time) over(partition by produce_machine_id) as produce_per_avgtime
        |from(
        |select
        |producerecordid as produce_record_id,
        |producemachineid as produce_machine_id,
        |(unix_timestamp(producecodeendtime) - unix_timestamp(producecodestarttime))  as produce_time
        |from data
        |) as r1
        |""".stripMargin)

    result.write.mode("overwrite")
      .saveAsTable("dws.machine_produce_per_avgtime")

    spark.close()
  }

}
